Although GAN-based methods can effectively address the data imbalance problem in fault diagnosis, data imbalance still exists in cross-domain fault diagnosis.

Jul 15, 2024 · First, an enhanced information generation method is introduced to produce realistic and useable synthetic data to mitigate data imbalance.

A multi-domain adversarial transfer network for cross domain fault diagnosis under imbalanced data ... Authors: Guofa Li; Shaoyang Liu; Jialong He; Liang Wang ...

7 days ago · Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for ...

A class-imbalance adversarial transfer learning (CIATL) network with input being imbalanced data to learn domain-invariant and knowledge and extends the ...

Apr 29, 2022 · A multi-domain weighted adversarial transfer network is proposed for the cross-domain intelligent fault diagnosis of bearings.

In this work, we propose a self-degraded contrastive domain adaptation (Sd-CDA) diagnosis framework to handle the domain discrepancy under the bi-imbalanced ...

Aug 5, 2024 · In this paper, we proposed a cross-domain fault diagnosis network based on a dual classifier (CFDNet) with input being limited and unbalanced data to learn ...

A deep transfer learning method for bearing fault diagnosis based on domain separation and adversarial learning.

Oct 16, 2023 · This paper proposes a bearing fault diagnosis method based on Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC) and deep transfer ...